TL;DR Article claims: Towns saw an increase in Anti-Refugee attacks if the average
daily Facebook use of that town was higher than for 84% of all people nationally.
That percentage is related to the number of people, not to the average
time spent on use of Facebook. (Conditions apply, see below.)
I quickly browsed the scientific paper to find what they measured, but realized I would have to read it more or less in full to understand it fully. They have somehow used Germany’s most popular Facebook page (Nutella Germany) to gauge general Facebook use.
So I’ll just speculate
Suppose they managed to estimate the time spent on Facebook per user per day. Then they computed the average of that, arriving at a national average and an average per town.
Now, about the “standard deviation”: of course, the time a person spends on Facebook is usually not the average for the population, but how far off from the average are they?
One way to express that would be as a percentage of the average. Say the average is 2 hours and a person spends 2 hours 30 minutes. Then that would be 25% higher than the average.
That’s easy to understand intuitively. The problem is that it doesn’t say anything about how common it is to be 25% above the average value - and it depends on the average. So 30 minutes is 25% of 2 hours, but only 5% of 10 hours.
Another way is relating to the rest of the population. Pick a limit on either side of the average, and count how many people fall within this range. Say 50% use Facebook between 1:50 and 2:15 per day.
This is what the standard deviation describes. The standard deviation also factors in how the data is spread around the average, which is often a good thing.
When we don’t know the exact distribution, assuming normal distribution might be ok. In that case, one standard deviation means those 68% of people closest to the average (34% below, 34% above).
Being above one standard deviation means a person belongs to the 16% most active Facebook users, or that 84% of the people use Facebook less than that person.
In the report, those towns had an average use higher than what 84% of all the people (nationally) had.
That means they either had This could be the case if, for example, there was a group of extremely active people or a larger part of their inhabitants fell within the 16% most active users as seen nation-wide.
- I have not looked into the actual measure of activity used in the report
- I have not looked into the actual distribution of the data, so those specific percentages may not apply
- I have not checked if claims in the article have support in the report
- Statistical correlation does not mean that one thing caused the other, there could be a hidden cause affecting both things